3 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
Penalty decomposition derivative free method for the minimization of partially separable functions over a convex feasible set
Francesco Cecere, Matteo Lapucci, Davide Pucci +1
In this paper, we consider the problem of minimizing a smooth function, given as finite sum of black-box functions, over a convex set. In order to advantageously exploit the struct…
On the Computation of the Efficient Frontier in Advanced Sparse Portfolio Optimization
Arturo Annunziata, Matteo Lapucci, Pieluigi Mansueto +1
In this work, we deal with the problem of computing a comprehensive front of efficient solutions in multi-objective portfolio optimization problems in presence of sparsity constrai…
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
Matteo Lapucci, Davide Pucci
In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest li…
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
Matteo Lapucci, Davide Pucci
In this paper, we deal with algorithms to solve the finite-sum problems related to fitting over-parametrized models, that typically satisfy the interpolation condition. In particul…
Effective Front-Descent Algorithms with Convergence Guarantees
Matteo Lapucci, Pierluigi Mansueto, Davide Pucci
In this manuscript, we address continuous unconstrained multi-objective optimization problems and we discuss descent type methods for the reconstruction of the Pareto set. Specific…
A Globally Convergent Gradient Method with Momentum
Matteo Lapucci, Giampaolo Liuzzi, Stefano Lucidi +2
In this work, we consider smooth unconstrained optimization problems and we deal with the class of gradient methods with momentum, i.e., descent algorithms where the search directi…